Registration of Pathological Images

  • Xiao Yang
  • Xu Han
  • Eunbyung Park
  • Stephen Aylward
  • Roland Kwitt
  • Marc Niethammer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9968)


This paper proposes an approach to improve atlas-to-image registration accuracy with large pathologies. Instead of directly registering an atlas to a pathological image, the method learns a mapping from the pathological image to a quasi-normal image, for which more accurate registration is possible. Specifically, the method uses a deep variational convolutional encoder-decoder network to learn the mapping. Furthermore, the method estimates local mapping uncertainty through network inference statistics and uses those estimates to down-weight the image registration similarity measure in areas of high uncertainty. The performance of the method is quantified using synthetic brain tumor images and images from the brain tumor segmentation challenge (BRATS 2015).


Lesion Area Structure Noise Target Registration Error Deformable Image Registration Variational Posterior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Xiao Yang
    • 1
  • Xu Han
    • 1
  • Eunbyung Park
    • 1
  • Stephen Aylward
    • 3
  • Roland Kwitt
    • 4
  • Marc Niethammer
    • 1
    • 2
  1. 1.UNC Chapel HillChapel HillUSA
  2. 2.Biomedical Research Imaging CenterChapel HillUSA
  3. 3.Kitware, Inc.CarrboroUSA
  4. 4.Department of Computer ScienceUniversity of SalzburgSalzburgAustria

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